Diabetic Kidney Disease Group Research Articles

Detection value of FOXO1 gene methylation, blood glucose and lipids in patients with type 2 diabetic kidney disease.

Forkhead transcription factor O1 (FOXO1) methylation is associated with inflammation. Diabetic kidney disease (DKD) is characterized with increased inflammatory markers such as uric acid, hemogram indices, C-reactive protein derived markers, omentin and neuregulin. This study aimed to investigate the effect of DNA methylation in FOXO1 gene promoter, blood glucose and lipids in the process of type 2 DKD. Bisulfite genomic sequencing was used to monitor DNA methylation in the promoter region (+1021, +1193) of FOXO1 gene. The detections were taken in glycosylated hemoglobin A1c, fasting plasma glucose and blood lipid. 81 participants were divided into the control group, the preliminary diabetes mellitus group, the pure diabetes mellitus group, and the DKD group. The other groups displayed higher fasting plasma glucose than the control group (all P value < .05). The fasting plasma glucose level was higher in the pure diabetes mellitus group than the preliminary diabetes mellitus group (P = .004). The levels of HbA1c were higher in other groups than control group and preliminary diabetes mellitus groups (all P values < .01). The high-density lipoprotein level was lower in the DKD group (P = .021, P = .022) than control and pure diabetes mellitus group. The levels of low-density lipoprotein were statistically lower in preliminary diabetes mellitus and DKD groups than control group (all P value < .02). Along with the progress of DKD, a down trend was observed in the total methylation rate of FOXO1 gene (P = .025), which contains 5 CpG sites (1021, +1193) in the promoter. Hypomethylation in the promoter of FOXO1 gene, hyperglycemia and low level of serum lipid might be associated with the pathogenesis of type 2 DKD.

Single nucleotide polymorphisms in the GFR-related gene and the SNP-SNP interactions on the risk of diabetic kidney disease in Chinese Han population.

Genetic susceptibilityisanimportant pathogenic mechanism in diabetic kidney disease (DKD). However, the specific gene variant associatedwith DKD susceptibility remains unclear. Glomerular filtration rate (GFR), an important indicator for the process of DKD, has a heritable component. This study aimed to explore whether these GFR-related single nucleotide polymorphisms (SNPs) were associated with DKD. GFR-related SNPs were collected from the Phenotype-Genotype Integrator (PheGenI) database. SNPs for population cohort analysis were selected following the criteria of complete records of eQTL and MAF > 5% in the Chinese Han population. Totally 498 subjects participated, including166 patients with DKD, 166 patients with T2DM, and 166 controls. The genotypes of SNPs were determined using a Sequenom MassARRAY system. Plink software was employed to analyze the SNP-SNP interactions. By screening the GFR-related SNPs recorded in the PheGenI database, four SNPs (rs1260326, rs17319721, rs35716097, and rs6420094) werefinally selected to investigate the association with DKD. It was shown that one of the four SNPs was related to DKD. The G allele of SLC34A1 rs6420094 was associated with a decreased risk of DKD in DKD and T2DM groups (OR 0.716; P = 0.049). Genetic model analysis revealed that rs6420094 was a protective factor for DKD in T2DM in a dominant model and an additive model (P = 0.03; P = 0.032, respectively). Although rs17319721 was not associated with the risk of DKD, the SNP-SNP interactions between rs17319721 and rs6420094 predicted a significantly decreased risk of DKD (OR 0.464; P = 0.047). SLC34A1 rs6420094 was associated with a decreased DKD risk in the Chinese Han population. SNP-SNP interaction between rs17319721 and rs6420094 was associated with a lower risk of DKD.

Urine metabolomics reveals biomarkers and the underlying pathogenesis of diabetic kidney disease.

Diabetic kidney disease (DKD) is the most common complication of type 2 diabetes mellitus (T2DM), and its pathogenesis is not yet fully understood and lacks noninvasive and effective diagnostic biomarkers. In this study, we performed urine metabolomics to identify biomarkers for DKD and to clarify the potential mechanisms associated with disease progression. We applied a liquid chromatography-mass spectrometry-based metabolomics method combined with bioinformatics analysis to investigate the urine metabolism characteristics of 79 participants, including healthy subjects (n = 20), T2DM patients (n = 20), 39 DKD patients that included 19 DKD with microalbuminuria (DKD + micro) and 20 DKD with macroalbuminuria (DKD + macro). Seventeen metabolites were identified between T2DM and DKD that were involved in amino acid, purine, nucleotide and primarily bile acid metabolism. Ultimately, a combined model consisting of 2 metabolites (tyramine and phenylalanylproline) was established, which had optimal diagnostic performance (area under the curve (AUC) = 0.94). We also identified 19 metabolites thatwere co-expressed within the DKD groups and 41 metabolites specifically expressed in the DKD + macro group. Ingenuity pathway analysis revealed three interaction networks of these 60 metabolites, involving the sirtuin signaling pathway and ferroptosis signaling pathway, as well as the downregulation of organic anion transporter 1, which may be important mechanisms that mediate the progression of DKD. This work reveals the metabolic alterations in T2DM and DKD, constructs a combined model to distinguish them and delivers a novel strategy for studying the underlying mechanism and treatment of DKD.

Impaired Amino Acid Metabolism and Its Correlation with Diabetic Kidney Disease Progression in Type 2 Diabetes Mellitus.

Background: Metabolomics is useful in elucidating the progression of diabetes; however, the follow-up changes in metabolomics among health, diabetes mellitus, and diabetic kidney disease (DKD) have not been reported. This study was aimed to reveal metabolomic signatures in diabetes development and progression. Methods: In this cross-sectional study, we compared healthy (n = 30), type 2 diabetes mellitus (T2DM) (n = 30), and DKD (n = 30) subjects with the goal of identifying gradual altering metabolites. Then, a prospective study was performed in T2DM patients to evaluate these altered metabolites in the onset of DKD. Logistic regression was conducted to predict rapid eGFR decline in T2DM subjects using altered metabolites. The prospective association of metabolites with the risk of developing DKD was examined using logistic regression and restricted cubic spline regression models. Results: In this cross-sectional study, impaired amino acid metabolism was the main metabolic signature in the onset and development of diabetes, which was characterized by increased N-acetylaspartic acid, L-valine, isoleucine, asparagine, betaine, and L-methionine levels in both the T2DM and DKD groups. These candidate metabolites could distinguish the DKD group from the T2DM group. In the follow-up study, higher baseline levels of L-valine and isoleucine were significantly associated with an increased risk of rapid eGFR decline in T2DM patients. Of these, L-valine and isoleucine were independent risk factors for the development of DKD. Notably, nonlinear associations were also observed for higher baseline levels of L-valine and isoleucine, with an increased risk of DKD among patients with T2DM. Conclusion: Amino acid metabolism was disturbed in diabetes, and N-acetylaspartic acid, L-valine, isoleucine, asparagine, betaine, and L-methionine could be biomarkers for the onset and progression of diabetes. Furthermore, high levels of L-valine and isoleucine may be risk factors for DKD development.

Prognosis and risk factors of chronic kidney disease progression in patients with diabetic kidney disease and non-diabetic kidney disease: a prospective cohort CKD-ROUTE study

Diabetic kidney disease (DKD) is emerging rapidly as the leading cause of chronic kidney disease (CKD) worldwide. In this 3-year prospective, multicenter cohort study, a total of 1138 pre-dialysis CKD patients were recruited. Patients were categorized into two groups according to the etiologies of DKD and non-diabetic kidney disease (NDKD). Propensity score matching was performed to adjust for confounding factors, resulting in 197 patients being assigned to DKD and NDKD groups, respectively. The primary endpoints were 50% estimated glomerular filtration rate (eGFR) decline and initiation of kidney replacement therapy (KRT). The secondary endpoints were all-cause death and the development of cardiovascular disease (CVD) events. We found that DKD patients have a higher risk to develop 50% eGFR decline endpoint (HR:2.30, 95%CI [1.48–3.58], p < 0.001) and KRT endpoint (HR:1.64, 95%CI [1.13–2.37], p < 0.05) than NDKD patients. The 3-year cumulative incidence of 50% eGFR decline and KRT endpoint was significantly higher in DKD patients (26.90% vs.13.71% and 35.03% vs. 22.34%, respectively). The Cox regression analyses showed that the increased systolic blood pressure (SBP), DKD, decreased serum albumin (Alb), and higher CKD stages were risk factors for the 50% eGFR decline endpoint; the increased SBP, DKD, decreased serum Alb, serum creatinine (Scr), higher CKD stages, presence of proteinuria and CVD were risk factors for KRT endpoint; the increased age, decreased hemoglobin (Hb), decreased serum Alb were risk factors for all-cause death endpoint; the increased age, decreased serum Alb were risk factors for CVD events endpoint. Appropriate preventive or therapeutic interventions should be taken to control these predictive factors to delay the development of CKD complications, thereby improving the prognosis and reducing the disease burden of the high-risk populations.

A comparative analysis of conventional and speckle-tracking strain echocardiographic findings in diabetic and non-diabetic kidney disease patients with normal ejection fraction

This study aimed to compare the differences in echocardiographic and strain parameters in patients with diabetic kidney disease (DKD) and non-diabetic kidney disease (NDKD) in a cohort with pre-dialysis chronic kidney disease (CKD) and normal ejection fraction (EF). In this single-center prospective study, patients with CKD stages 3–5 and EF > 55% were included. We compared cardiac structure and function using conventional and speckle-tracking strain echocardiography among DKD and NDKD groups. Cardiovascular outcomes were assessed at the end of the study. Of the included 117 patients, 56 (47.9%) had DKD, and 61 (52.1%) had NDKD. Patients with DKD had higher ratios of early mitral inflow velocity and mitral annular early diastolic velocity (E/e’) (11.9 ± 4.4 vs. 9.8 ± 3.5; p = 0.004), lower septal e’ velocity (7.1 ± 2.5 vs. 8.2 ± 2.8; p = 0.031), lower lateral e’ velocity (9.2 ± 2.9 vs. 10.4 ± 3.8; p = 0.045) and longer deceleration times (209.2 ± 41.5 vs. 189.1 ± 48.0; p = 0.017), compared to those with NDKD. Left ventricular mass index (LVMI), global longitudinal strain (GLS), early diastolic strain rate (SRE), and E/SRE were similar. At a median follow-up of 239 days, 3-P MACE (11.5% vs. 4.9%; p = 0.047) and 4-P MACE (28.6% vs. 11.5%; p = 0.020) were observed to be higher in the DKD group. Diastolic dysfunction was more common in patients with DKD, compared to those with NDKD, although both groups had similar LVMI and GLS. Those with DKD also had poorer cardiovascular outcomes. This highlights the importance of the assessment of diastolic function in CKD, particularly in those with diabetic CKD.

Association between kidney biopsy findings in CKD patients withdiabetes and renal replacement therapy initiation, a perspective from a low-middle income country.

Kidney disease in diabetes mellitus is usually explained by diabetic kidney disease, but other superimposed etiologies occur frequently. The distinction between diabetic kidney disease and non-diabetic kidney disease can only be made by performing kidney biopsy. Our objective was to evaluate the association of diabetic kidney disease, non-diabetic kidney disease, or both with renal replacement therapy initiation. This is a retrospective cohort that included patients with type 2 diabetes mellitus for whom a kidney biopsy was indicated. Subjects were followed-up for 5years, until renal replacement therapy initiation or were lost to follow up. One hundred and forty-one patients were included, 53 (39%) had diabetic kidney disease, 13 (9%) had non-diabetic kidney disease and 75 (54%) had both. Ninety-four percent of the cohort initiated renal replacement therapy during the 5-year follow-up. Higher degree of fibrosis was associated with a trend towards higher risk of requiring renal replacement therapy. In addition, the combined diabetic kidney disease + non-diabetic kidney disease group was associated with higher need of renal replacement therapy initiation when compared to the diabetic kidney disease group.

Screening and Identification of Hub Genes in the Development of Early Diabetic Kidney Disease Based on Weighted Gene Co-Expression Network Analysis.

ObjectiveThe study aimed to screen key genes in early diabetic kidney disease (DKD) and predict their biological functions and signaling pathways using bioinformatics analysis of gene chips interrelated to early DKD in the Gene Expression Omnibus database.MethodsGene chip data for early DKD was obtained from the Gene Expression Omnibus expression profile database. We analyzed differentially expressed genes (DEGs) between patients with early DKD and healthy controls using the R language. For the screened DEGs, we predicted the biological functions and relevant signaling pathways by enrichment analysis of Gene Ontology (GO) biological functions and Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathways. Using the STRING database and Cytoscape software, we constructed a protein interaction network to screen hub pathogenic genes. Finally, we performed immunohistochemistry on kidney specimens from the Beijing Hospital to verify the above findings.ResultsA total of 267 differential genes were obtained using GSE142025, namely, 176 upregulated and 91 downregulated genes. GO functional annotation enrichment analysis indicated that the DEGs were mainly involved in immune inflammatory response and cytokine effects. KEGG pathway analysis indicated that C-C receptor interactions and the IL-17 signaling pathway are essential for early DKD. We identified FOS, EGR1, ATF3, and JUN as hub sites of protein interactions using a protein–protein interaction network and module analysis. We performed immunohistochemistry (IHC) on five samples of early DKD and three normal samples from the Beijing Hospital to label the proteins. This demonstrated that FOS, EGR1, ATF3, and JUN in the early DKD group were significantly downregulated.ConclusionThe four hub genes FOS, EGR1, ATF3, and JUN were strongly associated with the infiltration of monocytes, M2 macrophages, and T regulatory cells in early DKD samples. We revealed that the expression of immune response or inflammatory genes was suppressed in early DKD. Meanwhile, the FOS group of low-expression genes showed that the activated biological functions included mRNA methylation, insulin receptor binding, and protein kinase A binding. These genes and pathways may serve as potential targets for treating early DKD.

Shear wave elastography in early diabetic kidney disease

SUMMARYOBJECTIVE:This study aimed to analyze the kidneys among the subjects with early stages of type 2 diabetic kidney disease by shear wave elastography quantitatively.METHODS:A total of 108 patients with type 2 diabetic kidney disease and 17 control subjects were enrolled. According to the estimated glomerular filtration rate and urinary albumin-to-urinary creatinine ratio, patients were classified into stages 1 to 3 diabetic kidney disease. Grayscale ultrasound andshear wave elastography were performed. The sizes, depths, and shear wave elastography values were recorded. These parameters were compared between the diabetic kidney disease and the control subjects.RESULTS:The mean shear wave elastography values were significantly higher in the diabetic kidney disease group (10.156±1.75 kPa vs. 8.241±1.4 kPa; p<0.001). We obtained statistically significantly higher shear wave elastography values in stages 2 and 3 diabetic kidney disease subjects than control subjects and in patients with stage 3 diabetic kidney disease compared to those with stage 1 diabetic kidney disease (p<0.05 for all). We obtained a cutoff value of 9.23 kPa for predicting diabetic kidney disease in early stages, with a sensitivity of 67% and a specificity of 82%.CONCLUSION:Shear wave elastography may be used as a noninvasive, simple, and quantitative method to provide diagnostic information as a part of routine management of patients with type 2 diabetes mellitus, especially in the early stages of diabetic kidney disease.

Association Between Serum Homocysteine Levels and Severity of Diabetic Kidney Disease in 489 Patients with Type 2 Diabetes Mellitus: A Single-Center Study.

BackgroundThis study from a single center aimed to investigate the association between serum homocysteine (Hcy) levels and severity of diabetic kidney disease (DKD) in 489 patients with type 2 diabetes mellitus (T2DM).Material/MethodsA total of 1163 patients with T2DM, including 674 T2DM without kidney disease (T2DM group) and 489 T2DM with DKD (DKD group), were evaluated. The DKD group was subdivided according to the chronic kidney disease (CKD) and albuminuria staging criteria in “Kidney Disease: Improving Global Outcomes 2012 clinical practice guideline” to quantify the severity of DKD: CKD stage 1 (n=164), CKD stage 2 (n=103), CKD stage 3 (n=95), CKD stage 4 (n=71), and CKD stage 5 (n=56) and stage A1 (n=57), stage A2 (n=250), and stage A3 (n=182), respectively. Peripheral blood was collected after 8 hours of fasting to test Hcy levels.ResultsIn CKD stages, Hcy levels gradually increased with increasing DKD severity (CKD stage 1 to 5); while in albuminuria stages, Hcy levels did not gradually increase with increasing DKD severity (stage A1 to A3). Hcy was an independent risk factor for CKD stages 2–5 (P<0.05), and had no effect on the albuminuria stages (P>0.05), while it may indirectly affect the occurrence of albuminuria stages through its impact on estimated glomerular filtration rate.ConclusionsThe relationship between Hcy level and DKD severity was related to the different staging methods used. Hcy was an independent risk factor for CKD stages but not albuminuria stages.

MO632: Impact of Diabetic Kidney Disease on Coronary Artery Plaque Progression

Abstract BACKGROUND AND AIMS The aim of this study was to evaluate the relationship between diabetic kidney disease (DKD) and change in coronary plaque volume in asymptomatic diabetes patients. METHOD In this prospective observational study, we analyzed 79 asymptomatic type 2 diabetes patients (age, 63.7 ± 8.1 years; male, 69.3%) without prior history of coronary artery disease who underwent serial 320-row computed tomography coronary angiography with an interscan interval of more than 24 months (median 37.7 months). Total coronary plaque volume was assessed at baseline and after 3 years, using QAngio CT (MEDIS) for vessel diameters greater than 1.5 mm in the three main branches. The primary endpoint was coronary plaque progression (PP), which was defined as the absolute change in coronary plaque volume during the 3 years. PP was compared among four groups, which were divided according to the presence or absence of DKD at baseline and after 3 years, as follows: Group 1 (no DKD at baseline, no DKD after 3 years), Group 2 (DKD at baseline, no DKD after 3 years), Group 3 (no DKD at baseline, DKD after 3 years), Group 4 (DKD at baseline, DKD after 3 years)]. DKD was defined as albuminuria &amp;gt;30 mg/g Cr or eGFR &amp;lt;60 mL/min/1.73 m2. We conducted univariate linear regression analysis to assess the association of DKD groups with PP. RESULTS Plaque volumes were significantly greater in Group 4 than in Group 1 both at baseline and after 3 years (Table 1). PP was also significantly greater in Group 4 than in Group 1 (β-coefficient: 293.6, P &amp;lt; 0.01). Group 4 had greater PP compared with Group 1 after adjustment (β-coefficient: 181.6, P &amp;lt; 0.05). CONCLUSION DKD may be an independent risk factor for coronary plaque progression in asymptomatic type 2 diabetic patients without a history of cardiovascular disease. Early therapeutic intervention in such patients may prevent the onset of coronary artery disease.

Diabetic kidney disease versus non-diabetic kidney disease in type 2 diabetic patients on dialysis: An observational cohort.

BackgroundAll chronic kidney diseases in diabetic patients are not diabetic kidney diseases. The objective was to compare the clinical characteristics, survival and access to transplantation in diabetic patients starting dialysis and classified either as diabetic kidney disease (DKD) or non‐diabetic kidney disease in diabetic patients (NDKD).MethodsWe used the nationwide French REIN registry to analyse baseline clinical characteristics at dialysis inception and outcomes defined as kidney transplantation, deaths and their causes. The probability of death or transplantation was analysed using a multivariate Cox model and the Fine and Gray competing for risk model (sdHT).ResultsWe included 65,136 patients from January 2009 to December 2015 with a median follow‐up of 31 months. The cumulative incidence of kidney transplantation over eight years was 46.9% (44.8–48.9) in non‐diabetic patients (ND), higher than the 19.3% (17.5–21.2) in the DKD group and 22.2% (18.4–26.7) in the NDKD group. The risk of death was significantly higher in the NDKD group than in the DKD group, even after accounting for the competing risk of transplantation (NDKD/sdHR 1.22; 95%CI 1.18–1.27; p < 0.005 vs. DKD/sdHR 1.12; 95%CI 1.08–1.16; p < 0.005 with adjustment for age, sex, major adverse cardiovascular events, cancer and chronic respiratory failure, compared to ND).ConclusionsIn diabetic patients starting dialysis, patients in the DKD group had reduced access to kidney transplantation. NDKD patients had a higher risk of mortality than DKD. The distinction between DKD and NDKD should be accounted for in the plan of care of diabetic patients starting dialysis.

The Impact of lncRNA on Diabetic Kidney Disease: Systematic Review and In Silico Analyses.

Background Long noncoding RNA (lncRNA) is involved in the occurrence and development of diabetic kidney disease (DKD). It is necessary to identify the expression of lncRNA from DKD patients through systematic reviews, and then carry out silico analyses to recognize the dysregulated lncRNA and their associated pathways. Methods The study searched Pubmed, Embase, Cochrane Library, WanFang, VIP, CNKI, and CBM to find lncRNA studies on DKD published before March 1, 2021. Systematic review of the literature on this topic was conducted to determine the expression of lncRNA in DKD and non-DKD controls. For the dysregulated lncRNA in DKD patients, silico analysis was performed, and lncRNA2Target v2.0 and starBase were used to search for potential target genes of lncRNA. The Encyclopedia of Genomics (KEGG) pathway enrichment analysis was performed to better identify dysregulated lncRNAs in DKD and determine the associated signal pathways. Results According to the inclusion and exclusion criteria, 28 publications meeting the eligibility criteria were included in the systematic evaluation. A total of 3,394 patients were enrolled in this study, including 1,238 patients in DKD group, and 1,223 diabetic patients, and 933 healthy adults in control group. Compared with the control, there were eight lncRNA disorders in DKD patients (MALAT1, GAS5, MIAT, CASC2, NEAT1, NR_033515, ARAP1-AS2, and ARAP1-AS1). In addition, five lncRNAs (MALAT1, GAS5, MIAT, CASC2, and NEAT1) participated in disease-related signal pathways, indicating their role in DKD. Discussion. This study showed that there were eight lncRNAs in DKD that were persistently dysregulated, especially five lncRNAs which were closely related to the disease. Although systematic review included 28 studies that analyzed the expression of lncRNA in DKD-related tissues, the potential of these dysregulated lncRNAs as biomarkers or therapeutic targets for DKD remains to be further explored. Trial registration. PROSPERO (CRD42021248634).

Glucagon-Like Peptide 1 Receptor Agonist Improves Renal Tubular Damage in Mice with Diabetic Kidney Disease.

PurposeThis study aims to investigate the renal protective effect of glucagon-like peptide 1 receptor agonist (GLP-1RA) on improving renal tubular damage in diabetic kidney disease (DKD) and to explore the potential mechanism of GLP-1RA on renal tubular protection.MethodsLong-acting GLP-1RA was used to treat DKD mice for 12 weeks. The label-free quantitative proteomic analysis of renal proteins was conducted to explore the differentially expressed proteins (DEPs) in the renal tissues of the control, DKD and GLP-1RA groups. The DEPs and markers of renal tubular injury were verified by qPCR in vivo and in vitro. The expression of glucagon-likepeptide-1 receptor (GLP-1R) in renal tubules was determined by immunofluorescence staining.ResultsGLP-1RA treatment significantly improved the tubular damages in kidney tissues of DKD mice and mTEC cells stimulated by high glucose (HG). Proteomics analysis revealed that 30 proteins in kidney tissue were differentially expressed among three groups. Seminal vesicle secretory protein 6 (SVS6) was the most differentially expressed protein in kidney tissues among three groups of mice. The expression changes of Svs6 mRNA in vitro and in vivo detected by qPCR were consistent with the results of proteomic analysis. Furthermore, reduction of Svs6 expression by SVS6 siRNA could attenuate HG-stimulated tubular injury in mTEC cells. Immunofluorescence staining also found that GLP-1R was widely expressed in renal tubules in vitro and in vivo.ConclusionGLP-1RA significantly improved renal tubular damage in DKD mice. SVS6 may be a potential therapeutic target for GLP-1RA in the treatment of DKD.

Serum lutein is a promising biomarker for type 2 diabetes mellitus and diabetic kidney disease in the elderly.

ObjectiveTo investigate the relationship between serum lutein and type 2 diabetes mellitus (T2DM) and diabetic kidney disease (DKD) in elderly individuals.MethodsA total of 60 T2DM patients over 60 years were subgrouped into a DKD group and a non‐DKD group according to their urinary microalbumin‐to‐creatinine ratio (UACR), while 30 age‐matched non‐T2DM patients were recruited in the control group. Baseline characteristics, laboratory examination results, and serum lutein levels were compared, and their correlations were analyzed. Receiver operating characteristic (ROC) curves were plotted to identify the diagnostic potential of lutein in T2DM and DKD.ResultsThe lutein level in the T2DM group was significantly lower than that in the control group and was also significantly lower in the DKD group than in the non‐DKD group (p < 0.001). Lutein levels were negatively correlated with body mass index, glycosylated hemoglobin, fasting blood glucose, triglyceride, and UACR and positively correlated with high‐density lipoprotein cholesterol (p < 0.05). T2DM patients were divided into four groups according to the quartile of their lutein level. The proportion of T2DM and DKD gradually decreased with increasing lutein levels (p < 0.001). The area under the ROC curve of serum lutein in diagnosing T2DM and DKD was 0.880 and 0.779, respectively, with corresponding cut‐off values of 0.433 μmol/L and 0.197 μmol/L (p < 0.001).ConclusionThe serum level of lutein is negatively correlated with the incidence of T2DM and DKD in the elderly and can serve as a diagnostic marker for T2DM and DKD.

Compositional Alterations of Gut Microbiota in Patients with Diabetic Kidney Disease and Type 2 Diabetes Mellitus

PurposeEmerging evidence has revealed that gut microbiota plays a pivotal role in the pathogenesis of type 2 diabetes mellitus (T2DM) and diabetic kidney disease (DKD). However, few studies have used metagenomic sequencing to analyze the alterations of gut microbiota community in patients with early-stage DKD.MethodsWe carried out metagenomic sequencing in fecal samples of 10 DKD patients (DKD group) and 10 T2DM patients who appeared to be less prone to DKD (non-DKD group), aiming to compare the composition and function of gut microbiota between the DKD and non-DKD groups.ResultsThe gut microbial community of the DKD group was significantly different from that of the non-DKD group, characterized by a marked increase in phylum Proteobacteria, genus Selenomonadales, Neosynechococcus, Shigella, Bilophila, Acidaminococcus, species, Escherichia coli, Bacteroides plebeius, Megasphaera elsdenii, Acidaminococcus unclassified, and Bilophila wadsworthia. The amounts of species Citrobacter farmeri and Syntrophaceticus schinkii were significantly and positively correlated with the urinary albumin creatinine ratio in the DKD group. Furthermore, functional analysis based on dbCAN and KEGG databases showed aberrant lipopolysaccharide (LPS) biosynthesis and carbohydrate metabolism in the gut microbiome of the DKD group.ConclusionOur findings provided evidence for alterations in the composition and function of gut microbiota in patients with DKD versus the non-DKD group. These data may contribute to a more comprehensive understanding of the pathological mechanisms of DKD.

Peripheral nerve morphology and intraneural blood flow in chronic kidney disease with and without diabetes.

Introduction/AimsSonographic alterations of peripheral nerves in pre‐dialytic kidney disease are yet to be determined. We aimed to assess peripheral nerve cross‐sectional area (CSA) and intraneural blood flow in patients with pre‐dialytic chronic kidney disease (CKD) and diabetic kidney disease (DKD).MethodsSubjects with CKD (n = 20) or DKD (n = 20) underwent ultrasound to assess CSA of the median and tibial nerves as well as intraneural blood flow of the median nerve. Blood flow was quantified using maximum perfusion intensity. Neuropathy was assessed using the Total Neuropathy Score. A 6‐m timed walk test was also performed. Healthy controls (n = 28) were recruited for comparison.ResultsThe DKD group had more severe neuropathy (p = .024), larger tibial nerve CSA (p = .002) and greater median nerve blood flow than the CKD group (p = .023). Blood flow correlated with serum potassium in disease groups (r = 0.652, p = .022). Disease groups had larger tibial nerve CSA than controls (p < .05). No blood flow was detected in controls. Tibial nerve enlargement was associated with slower maximal walking speeds in disease groups (r = −0.389, p = .021).DiscussionSubjects with DKD demonstrated enlarged tibial nerve CSA and increased median nerve blood flow compared to those with CKD. Elevations in serum potassium were associated with increased blood flow. Sonographic alterations were detectable in pre‐dialytic kidney disease compared to controls, highlighting the utility of ultrasound in the assessment of nerve pathology in these patient groups.

Potential Role of the Renal Arterial Resistance Index in the Differential Diagnosis of Diabetic Kidney Disease.

AimsTo investigate the potential role of renal arterial resistance index (RI) in the differential diagnosis between diabetic kidney disease (DKD) and non-diabetic kidney disease (NDKD) and establish a better-quantified differential diagnostic model.Materials and MethodsWe consecutively reviewed 469 type 2 diabetes patients who underwent renal biopsy in our center. According to the renal biopsy results, eligible patients were classified into the DKD group and the NDKD group. The diagnostic significance of RI was evaluated by receiver operating characteristic (ROC) curve analysis. Logistic regression analysis was used to search for independent risk factors associated with DKD. Then a novel diagnostic model was established using multivariate logistic regression analysis.ResultsA total of 332 DKD and 137 NDKD patients were enrolled for analysis. RI was significantly higher in the DKD group compared with those in the NDKD group (0.70 vs. 0.63, p< 0.001). The optimum cutoff value of RI for predicting DKD was 0.66 with sensitivity (69.2%) and specificity (80.9%). Diabetic retinopathy, diabetes duration ≥ 60 months, HbA1c ≥ 7.0(%), RI ≥ 0.66, and body mass index showed statistical significance in the multivariate logistic regression analysis. Then, we constructed a new diagnostic model based on these results. And the validation tests indicated that the new model had good sensitivity (81.5%) and specificity (78.6%).ConclusionsRI has a potential role in discriminating DKD from NDKD. The RI-based predicting model can be helpful for differential diagnosis of DKD and NDKD.

Serum Trimethylamine N-oxide and the Diversity of the Intestinal Microbial Flora in Type 2 Diabetes Complicated by Diabetic Kidney Disease.

Trimethylamine N-oxide (TMAO) serves as a metabolite of intestinal bacteria as well as a urotoxin influencing the prognosis of chronic kidney disease (CKD), which has become a research hotspot in the field of kidney disease. This study preliminarily explored the alternations of the microbial flora and serum TMAO in patients with type 2 diabetes mellitus (T2DM) complicated with diabetic kidney disease (DKD). Seventeen T2DM patients at the Affiliated Hospital of Zunyi Medical University between September 2018 and February 2019 were included. Among these patients, 8 patients had T2DM complicated with DKD. Eight healthy volunteers constituted the control group. Fresh stool was collected for Illumina sequencing. Based on the sequencing outcomes, the flora diversity and species differences were analyzed. Serum TMAO, cystatin C, urinary albumin/urine creatinine ratios (ACRs), and routine biochemical outcomes were also compared. The DKD group exhibited a significantly higher TMAO level than the remaining groups. The high-TMAO group had a significantly increased ACR level compared with the low-TMAO group. TMAO positively correlated with the ACR. Compared with the control group, the DKD group exhibited a decreased flora diversity. At the genus level, both the T2DM group and the DKD group showed decreased numbers of Alloprevotella and Megasphaera compared with the control group. The difference in Megasphaera between the DKD group and the control group was significant. The alternation of the intestinal microbial flora may participate in the development of DKD, and TMAO and chronic inflammation might be important factors for DKD development.

The role of endothelial biomarkers in predicting damp-heat syndrome in diabetic kidney disease

To explore the role of endothelial biomarkers in predicting damp-heat syndrome in diabetic kidney disease (DKD). A total of 183 patients with DKD were divided into 3 groups: the early DKD group, established DKD group, and advanced DKD group. All patients were classified according to traditional Chinese medicine (TCM) syndrome type, and clinical indexes were collected for statistical analysis. A total of 183 DKD patients were included in this study. Fibroblast growth factor 23 (FGF23), chitinase-3-like protein 1 (CHI3L1), endocan, tumor necrosis factor receptor 1 (TNFR1), secretory leukocyte protease inhibitor (SLPI), and vascular endothelial growth factor A (VEGF-A) were increased in advanced DKD. FGF23, CHI3L1, endocan, SLPI, and TNFR1 showed a negative correlation with estimated glomerular filtration rate (eGFR), while they had a positive correlation with 24 h urine protein. After adjusting for age, gender, diabetes duration, body mass index (BMI), hemoglobin, glucose, uric acid, 24 h urine protein, cholesterol, triglyceride, low-density lipoprotein, and hemoglobin A1c (HbA1c), the multiple regression analysis showed that FGF23, endocan, TNFR1, and SLPI significantly correlated with eGFR. FGF23, endocan, TNFR1, and SLPI are elevated in advanced DKD compared with early stage, and they may take part in the pathogenesis and progression of DKD. Our study provides useful biomarkers for predicting the appearance of damp-heat syndrome, including FGF23, endocan, TNFR1, and SLPI.